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Data-driven analysis and Machine Learning (ML) algorithms can offer novel insights into tribological phenomena by establishing correlations between material and tribological properties. We developed ML algorithms using tribological data available in the literature for predicting the coefficient of friction (COF) and wear rate of self-lubricating aluminum-graphite (Al/Gr) composites. We collected data on effects of material variables (graphite content, hardness, ductility, yield strength, silicon carbide content, and tensile strength), processing procedure, heat treatment and tribological test variables (normal load, sliding speed, and sliding distance) on tribological properties and established two-parameter relationships. This data is analyzed using several ML algorithms: Artificial Neural Network (ANN), K Nearest Neighbor (KNN), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Random Forest (RF). The trained ML models can predict the tribological behavior from material variables and test conditions, beyond what is possible from two-parameter correlations. GBM outperformed other ML algorithms in predicting friction behavior, while RF had the best prediction of the wear behavior. ML analysis identified graphite content and hardness and as the most significant variables in predicting the COF, while graphite content and sliding speed were the most dominant variables for wear rates.

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In this article we provide an overview on the current and emerging applications of machine learning (ML) in the design, synthesis, and characterization of metal matrix composites (MMC). We have demonstrated that ML methods can be applied in three distinct categories, namely property prediction, microstructure analysis, and process optimization, which are associated with three major classes of ML techniques, i.e., regression, classification, and optimal control, respectively. ML algorithms have been successfully applied for prediction of mechanical, tribological, corrosion, and wetting properties of different MMCs. However, ML methods (e.g., computer vision, which is suitable for microstructural characterization and defect detections) and optimization algorithms (e.g., reinforcement learning) have not been fully utilized for design, processing, and characterization of metal matrix composites despite their enormous capacities. We conclude that ML methods are promising not only to predict various properties but also to automate microstructural analysis and optimization of manufacturing MMCs.

Cobalt (Co) catalyst is supported by carbon nanotubes (CNT) using a strong electrostatic adsorption (SEA) method. To promote activity and selectivity as well as find the optimum loading percentage and its effect on catalyst performance, manganese (Mn) has been added to the Co/CNT catalyst. Samples were characterized by a scanning electron microscope (SEM-EDX), transmission electron microscope (TEM), hydrogen temperature programmed reduction (H 2 -TPR), Zeta potential, Brunauer-Emmett-Teller (BET) analysis, X-ray diffraction (XRD), and X-ray spectroscopy (XPS). TEM images illustrated an intake of metal particles which were highly dispersed, having a narrow particle size distribution of 6-8 nm to the external and internal CNT support. H 2 -TPR showed a lower temperature reduction with Mn at 420 • C for Fischer-Tropsch synthesis (FTS) reaction. The Co-Mn/CNT catalyst performance test for FTS was performed at a temperature of 240 • C in a fixed-bed micro-reactor at a pressure of 2.0 MPa. The addition of manganese resulted in a lower methane selectivity and a higher C 5+ product with an optimum percentage of 5% of manganese. CO conversion was 86.6% and had a C 5+ selectivity of 81.5%, which was higher than the catalysts obtained using only Co on pretreated CNT.

Wetting of multiphase alloys and their composites depends on multiple parameters, and these relationships are difficult to predict from first principles only. We study correlations between the composition, surface finish, and microstructure of Al–Si alloys (Si content 7–50%) and Al metal matrix composites (MMCs) with graphite (Gr), NiAl3, and SiC and the water contact angle (CA) experimentally, theoretically, and with machine learning (ML) techniques. Their surface properties were modified by mechanical abrasion, etching, and addition of alloying elements. An ML approach was developed to investigate correlations between the predictor variables (properties of the materials) and the CA. Theoretical models of wetting of rough surfaces (Wenzel, Cassie–Baxter, and their modifications) do not fully capture the CA, while ML models follow the experimental values. A full factorial design is utilized with combinations of all levels of the predictor factors (grit size, silicon percentage, droplet size, elapsed time, etching, reinforcing particles). To map the predictor variables to the response variables, 409 experimental data points were applied to train and test various supervised ML models, namely, regression, artificial neural network (ANN), chi-square automatic interaction detection (CHAID), extreme gradient boosting (XGBoost), and random forest. The correlations between the most significant factors and CA are explored through visualization techniques. The most accurately trained model shows a strong positive linear correlation (r > 0.9) between predicted and observed CA values in the test set, indicating the robustness of the model. The experimental measurements and artificial intelligence results demonstrate that CA increases following mechanically abrading the surface, etching, and adding Gr to the surface. The ML methods are promising to predict wetting properties and to provide a deeper understanding of the physical phenomena associated with the wettability of metallic alloys and their metal matrix composites.

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